{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Image Classifier Project_colab.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.8"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"Mtoo_qQ-b2wb","colab_type":"code","outputId":"9452a869-4010-4798-84ab-e69b64d6ece2","executionInfo":{"status":"ok","timestamp":1559043743990,"user_tz":-330,"elapsed":8336,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":318}},"source":["!nvidia-smi"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Tue May 28 11:42:20 2019       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 418.67       Driver Version: 410.79       CUDA Version: 10.0     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|===============================+======================+======================|\n","|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   70C    P8    18W /  70W |      0MiB / 15079MiB |      0%      Default |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                       GPU Memory |\n","|  GPU       PID   Type   Process name                             Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"tFzuiLdncDHB","colab_type":"code","outputId":"db9197a0-d813-4554-9a01-4176cf431495","executionInfo":{"status":"ok","timestamp":1559043748367,"user_tz":-330,"elapsed":12681,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["!ls"],"execution_count":2,"outputs":[{"output_type":"stream","text":["sample_data\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"ycfmjiJRGYgb","colab_type":"code","outputId":"af19da9e-a1ad-471a-9aa4-29b3d920ae5c","executionInfo":{"status":"ok","timestamp":1559043787404,"user_tz":-330,"elapsed":38283,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":335}},"source":["!pip install PyDrive\n","import os\n","from pydrive.auth import GoogleAuth\n","from pydrive.drive import GoogleDrive\n","from google.colab import auth\n","from oauth2client.client import GoogleCredentials\n","auth.authenticate_user()\n","gauth = GoogleAuth()\n","gauth.credentials = GoogleCredentials.get_application_default()\n","drive = GoogleDrive(gauth)\n","\n","#https://drive.google.com/open?id=1kk0ytV9j_UEZWEDSubJN8ws-vwWh6TER\n","\n","download = drive.CreateFile({'id': '1kk0ytV9j_UEZWEDSubJN8ws-vwWh6TER'})\n","download.GetContentFile('flower_data.zip')\n","\n","#https://drive.google.com/open?id=1wPFrVNDbW6Th5bzMR-ySjlHDHtyxVkLO\n","\n","download = drive.CreateFile({'id': '1wPFrVNDbW6Th5bzMR-ySjlHDHtyxVkLO'})\n","download.GetContentFile('flower_data_original_test.zip')\n","\n","#https://drive.google.com/open?id=1sMSp2kYwoLAJ1S7zNAL-0W1ihYsvmBUd\n","download = drive.CreateFile({'id': '1sMSp2kYwoLAJ1S7zNAL-0W1ihYsvmBUd'})\n","download.GetContentFile('cat_to_name.json')\n"],"execution_count":3,"outputs":[{"output_type":"stream","text":["Collecting PyDrive\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/e0/0e64788e5dd58ce2d6934549676243dc69d982f198524be9b99e9c2a4fd5/PyDrive-1.3.1.tar.gz (987kB)\n","\r\u001b[K     |▎                               | 10kB 18.2MB/s eta 0:00:01\r\u001b[K     |▋                               | 20kB 7.0MB/s eta 0:00:01\r\u001b[K     |█                               | 30kB 9.8MB/s eta 0:00:01\r\u001b[K     |█▎                              | 40kB 6.1MB/s eta 0:00:01\r\u001b[K     |█▋                              | 51kB 7.5MB/s eta 0:00:01\r\u001b[K     |██                              | 61kB 8.8MB/s eta 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PyDrive) (1.6.7)\n","Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (4.1.3)\n","Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (3.13)\n","Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (3.0.0)\n","Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (1.12.0)\n","Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (0.11.3)\n","Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.4.5)\n","Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (4.0)\n","Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.2.5)\n","Building wheels for collected packages: PyDrive\n","  Building wheel for PyDrive (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Stored in directory: /root/.cache/pip/wheels/fa/d2/9a/d3b6b506c2da98289e5d417215ce34b696db856643bad779f4\n","Successfully built PyDrive\n","Installing collected packages: PyDrive\n","Successfully installed PyDrive-1.3.1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"t3vnRbdmJyTF","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uBS7sN2jI_Cw","colab_type":"code","outputId":"ef1f8a78-be8c-4bc9-f7e1-7e0f96af69a7","executionInfo":{"status":"ok","timestamp":1559043791008,"user_tz":-330,"elapsed":31504,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"source":["!ls"],"execution_count":4,"outputs":[{"output_type":"stream","text":["adc.json\t  flower_data_original_test.zip  sample_data\n","cat_to_name.json  flower_data.zip\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"scenAmc4K2m7","colab_type":"code","outputId":"07102954-18aa-41da-e9fd-c53b37c130b0","executionInfo":{"status":"ok","timestamp":1559043819294,"user_tz":-330,"elapsed":18821,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":123}},"source":["!ls -r flower_data_test"],"execution_count":9,"outputs":[{"output_type":"stream","text":["99  93\t88  82\t77  71\t66  60\t55  5\t44  39\t33  28\t22  17\t11\n","98  92\t87  81\t76  70\t65  6\t54  49\t43  38\t32  27\t21  16\t102\n","97  91\t86  80\t75  7\t64  59\t53  48\t42  37\t31  26\t20  15\t101\n","96  90\t85  8\t74  69\t63  58\t52  47\t41  36\t30  25\t2   14\t100\n","95  9\t84  79\t73  68\t62  57\t51  46\t40  35\t3   24\t19  13\t10\n","94  89\t83  78\t72  67\t61  56\t50  45\t4   34\t29  23\t18  12\t1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"bcHKgmACHcqw","colab_type":"code","colab":{}},"source":["!unzip flower_data.zip"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"At5Yxa5wKtM4","colab_type":"code","colab":{}},"source":["!mkdir flower_data_test"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"TFk2Ypt_JDFa","colab_type":"code","colab":{}},"source":["!unzip flower_data_original_test.zip -d flower_data_test"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"vVHkSjtoGZwn","colab_type":"code","colab":{}},"source":["!pip install -U -q PyDrive\n","\n","from pydrive.auth import GoogleAuth\n","from pydrive.drive import GoogleDrive\n","from google.colab import auth\n","from oauth2client.client import GoogleCredentials\n","\n","import io\n","import zipfile\n","# Authenticate and create the PyDrive client.\n","# This only needs to be done once per notebook.\n","auth.authenticate_user()\n","gauth = GoogleAuth()\n","gauth.credentials = GoogleCredentials.get_application_default()\n","drive = GoogleDrive(gauth)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"gmXu9T9fGZs5","colab_type":"code","colab":{}},"source":["#uploadId = '1w26x8ZtouNfb2X9piy6cEqsgi83BNpCN'\n","#uploaded = drive.CreateFile({'parents':[{'id': uploadId}] , 'title' : 'flower_data.zip'})\n","#uploaded.SetContentFile('flower_data.zip')\n","#uploaded.Upload()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"VXblVyJzf2lX","colab_type":"text"},"source":["# Developing an AI application\n","\n","Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. \n","\n","In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using [this dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) of 102 flower categories, you can see a few examples below. \n","\n","<img src='assets/Flowers.png' width=500px>\n","\n","The project is broken down into multiple steps:\n","\n","* Load and preprocess the image dataset\n","* Train the image classifier on your dataset\n","* Use the trained classifier to predict image content\n","\n","We'll lead you through each part which you'll implement in Python.\n","\n","When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.\n","\n","First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here."]},{"cell_type":"code","metadata":{"id":"-wcEwfJIf2lc","colab_type":"code","colab":{}},"source":["# Imports here\n","import numpy as np\n","import torch.nn as nn\n","import torch.optim as optim\n","import torch\n","from torchvision import datasets,transforms,utils,models\n","import matplotlib.pyplot as plt\n","import os\n","import time\n","import copy"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"CYoDxw6Cf2ln","colab_type":"code","outputId":"b973997e-90a6-471d-9860-dbce9d7425c7","executionInfo":{"status":"ok","timestamp":1559043845603,"user_tz":-330,"elapsed":6342,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["!ls -r flower_data/"],"execution_count":12,"outputs":[{"output_type":"stream","text":["valid  train\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"WJT1Ey4hf2lz","colab_type":"text"},"source":["## Load the data\n","\n","Here you'll use `torchvision` to load the data ([documentation](http://pytorch.org/docs/0.3.0/torchvision/index.html)). You can [download the data here](https://s3.amazonaws.com/content.udacity-data.com/courses/nd188/flower_data.zip). The dataset is split into two parts, training and validation. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. If you use a pre-trained network, you'll also need to make sure the input data is resized to 224x224 pixels as required by the networks.\n","\n","The validation set is used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.\n","\n","The pre-trained networks available from `torchvision` were trained on the ImageNet dataset where each color channel was normalized separately. For both sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`, calculated from the ImageNet images.  These values will shift each color channel to be centered at 0 and range from -1 to 1."]},{"cell_type":"code","metadata":{"id":"JG7faG--f2l3","colab_type":"code","outputId":"776e2055-e12b-45e6-dbec-4485cd6ee6a5","executionInfo":{"status":"ok","timestamp":1559043845613,"user_tz":-330,"elapsed":2341,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["data_dir = 'flower_data'\n","train_dir = data_dir + '/train'\n","valid_dir = data_dir + '/valid'\n","\n","datadict={\n","    'train':train_dir,\n","    'valid':valid_dir\n","}\n","datadict['train']"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'flower_data/train'"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"nvapZ0H6f2mO","colab_type":"code","colab":{}},"source":["# TODO: Define your transforms for the training and validation sets\n","batch_size=16\n","\n","data_transforms = {\n","    'train': transforms.Compose([\n","        transforms.RandomRotation(45),\n","        transforms.RandomResizedCrop(224),\n","        transforms.RandomHorizontalFlip(),\n","        transforms.ToTensor(),\n","        transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])\n","    ]),\n","    'valid':transforms.Compose([\n","        transforms.Resize(256),\n","        transforms.CenterCrop(224),\n","        transforms.ToTensor(),\n","        transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])\n","}\n","\n","# TODO: Load the datasets with ImageFolder\n","image_datasets = {i:datasets.ImageFolder(datadict[i],data_transforms[i]) for i in ['train','valid']}\n","\n","# TODO: Using the image datasets and the trainforms, define the dataloaders\n","dataloaders = {i:torch.utils.data.DataLoader(image_datasets[i],batch_size=batch_size,shuffle=True) for i in ['train','valid']}\n","\n","dataset_sizes={i:len(image_datasets[i]) for i in ['train','valid']}\n","\n","class_names=image_datasets['train'].classes"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"3PIUYoS6f2Vn","colab_type":"code","outputId":"b123e8a0-041c-43a3-eea0-89638e736774","executionInfo":{"status":"ok","timestamp":1559043846160,"user_tz":-330,"elapsed":1037,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"source":["dataloaders"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'train': <torch.utils.data.dataloader.DataLoader at 0x7fcbfd2e3a20>,\n"," 'valid': <torch.utils.data.dataloader.DataLoader at 0x7fcbfd257390>}"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"markdown","metadata":{"id":"LW21zXvRf2mb","colab_type":"text"},"source":["### Label mapping\n","\n","You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers."]},{"cell_type":"code","metadata":{"id":"y2Y6AZXef2mi","colab_type":"code","colab":{}},"source":["import json\n","\n","with open('cat_to_name.json', 'r') as f:\n","    cat_to_name = json.load(f)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"scrolled":true,"id":"4CpoQ8mIf2mu","colab_type":"code","outputId":"f6698b8a-3d70-4dba-a56f-47e7d7ae4c90","executionInfo":{"status":"ok","timestamp":1559043850155,"user_tz":-330,"elapsed":1571,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":1821}},"source":["cat_to_name"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'1': 'pink primrose',\n"," '10': 'globe thistle',\n"," '100': 'blanket flower',\n"," '101': 'trumpet creeper',\n"," '102': 'blackberry lily',\n"," '11': 'snapdragon',\n"," '12': \"colt's foot\",\n"," '13': 'king protea',\n"," '14': 'spear thistle',\n"," '15': 'yellow iris',\n"," '16': 'globe-flower',\n"," '17': 'purple coneflower',\n"," '18': 'peruvian lily',\n"," '19': 'balloon flower',\n"," '2': 'hard-leaved pocket orchid',\n"," '20': 'giant white arum lily',\n"," '21': 'fire lily',\n"," '22': 'pincushion flower',\n"," '23': 'fritillary',\n"," '24': 'red ginger',\n"," '25': 'grape hyacinth',\n"," '26': 'corn poppy',\n"," '27': 'prince of wales feathers',\n"," '28': 'stemless gentian',\n"," '29': 'artichoke',\n"," '3': 'canterbury bells',\n"," '30': 'sweet william',\n"," '31': 'carnation',\n"," '32': 'garden phlox',\n"," '33': 'love in the mist',\n"," '34': 'mexican aster',\n"," '35': 'alpine sea holly',\n"," '36': 'ruby-lipped cattleya',\n"," '37': 'cape flower',\n"," '38': 'great masterwort',\n"," '39': 'siam tulip',\n"," '4': 'sweet pea',\n"," '40': 'lenten rose',\n"," '41': 'barbeton daisy',\n"," '42': 'daffodil',\n"," '43': 'sword lily',\n"," '44': 'poinsettia',\n"," '45': 'bolero deep blue',\n"," '46': 'wallflower',\n"," '47': 'marigold',\n"," '48': 'buttercup',\n"," '49': 'oxeye daisy',\n"," '5': 'english marigold',\n"," '50': 'common dandelion',\n"," '51': 'petunia',\n"," '52': 'wild pansy',\n"," '53': 'primula',\n"," '54': 'sunflower',\n"," '55': 'pelargonium',\n"," '56': 'bishop of llandaff',\n"," '57': 'gaura',\n"," '58': 'geranium',\n"," '59': 'orange dahlia',\n"," '6': 'tiger lily',\n"," '60': 'pink-yellow dahlia',\n"," '61': 'cautleya spicata',\n"," '62': 'japanese anemone',\n"," '63': 'black-eyed susan',\n"," '64': 'silverbush',\n"," '65': 'californian poppy',\n"," '66': 'osteospermum',\n"," '67': 'spring crocus',\n"," '68': 'bearded iris',\n"," '69': 'windflower',\n"," '7': 'moon orchid',\n"," '70': 'tree poppy',\n"," '71': 'gazania',\n"," '72': 'azalea',\n"," '73': 'water lily',\n"," '74': 'rose',\n"," '75': 'thorn apple',\n"," '76': 'morning glory',\n"," '77': 'passion flower',\n"," '78': 'lotus lotus',\n"," '79': 'toad lily',\n"," '8': 'bird of paradise',\n"," '80': 'anthurium',\n"," '81': 'frangipani',\n"," '82': 'clematis',\n"," '83': 'hibiscus',\n"," '84': 'columbine',\n"," '85': 'desert-rose',\n"," '86': 'tree mallow',\n"," '87': 'magnolia',\n"," '88': 'cyclamen',\n"," '89': 'watercress',\n"," '9': 'monkshood',\n"," '90': 'canna lily',\n"," '91': 'hippeastrum',\n"," '92': 'bee balm',\n"," '93': 'ball moss',\n"," '94': 'foxglove',\n"," '95': 'bougainvillea',\n"," '96': 'camellia',\n"," '97': 'mallow',\n"," '98': 'mexican petunia',\n"," '99': 'bromelia'}"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"id":"trZwCvcuf2m5","colab_type":"code","outputId":"33a69021-d53a-40cb-8157-a9dba68f1471","executionInfo":{"status":"ok","timestamp":1559043852829,"user_tz":-330,"elapsed":3569,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["fig,ax=plt.subplots(figsize=(10,10))\n","\n","def imshow(inp,title=None):\n","    inp=inp.numpy().transpose((1,2,0))\n","    mean=np.array([0.485, 0.456, 0.406])\n","    std=np.array([0.229, 0.224, 0.225])    \n","    \n","    inp=std*inp+mean\n","    inp=np.clip(inp,0,1)\n","    ax.imshow(inp)\n","    \n","    if title is not None:\n","        plt.title(title)\n","    plt.pause(0.1)\n","    \n","inputs,classes=next(iter(dataloaders['train']))\n","out=utils.make_grid(inputs)\n","\n","imshow(out,title=[cat_to_name[str(i.item())] for i in classes])"],"execution_count":18,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABTkAAAC7CAYAAACuN5oUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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xBDAOLqUTVKxwHIdIRSZMaxwR43wFxPNISPAclziJyfseKk4Io4jI8XEREhHjOENwRFDK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\nZjSckm8ZnHSU0wnjUtHZjoEf9r2SNrJcd4wnU7JCk1caGQdsbQtSa7g22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YGikKh8joyS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\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2n2X10VFajRZO0yPjCmY6AUcWApZXY2IJozWPi0/NIHyPXVvg/IWzNLsev/6B16PdHI/8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\nJEmSJEmSpJFmk1OSJEmSJEnSSPs/DfCoVPVE5e8AAAAASUVORK5CYII=\n","text/plain":["<Figure size 720x720 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"tC2aj56ff2nN","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"ODt_iGaqf2nX","colab_type":"code","outputId":"47ddd7c8-de52-4f20-b0d4-df2c2f011bc9","executionInfo":{"status":"ok","timestamp":1559043852842,"user_tz":-330,"elapsed":1643,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":300}},"source":["for x in classes:\n","    print(x.item(), cat_to_name[str(x.item())])"],"execution_count":19,"outputs":[{"output_type":"stream","text":["91 hippeastrum\n","49 oxeye daisy\n","70 tree poppy\n","53 primula\n","75 thorn apple\n","37 cape flower\n","70 tree poppy\n","42 daffodil\n","76 morning glory\n","91 hippeastrum\n","70 tree poppy\n","76 morning glory\n","31 carnation\n","77 passion flower\n","31 carnation\n","86 tree mallow\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Q5jm2sBTf2ns","colab_type":"text"},"source":["# Building and training the classifier\n","\n","Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from `torchvision.models` to get the image features. Build and train a new feed-forward classifier using those features.\n","\n","We're going to leave this part up to you. If you want to talk through it with someone, chat with your fellow students! You can also ask questions on the forums or join the instructors in office hours.\n","\n","Refer to [the rubric](https://review.udacity.com/#!/rubrics/1663/view) for guidance on successfully completing this section. Things you'll need to do:\n","\n","* Load a [pre-trained network](http://pytorch.org/docs/master/torchvision/models.html) (If you need a starting point, the VGG networks work great and are straightforward to use)\n","* Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout\n","* Train the classifier layers using backpropagation using the pre-trained network to get the features\n","* Track the loss and accuracy on the validation set to determine the best hyperparameters\n","\n","We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!\n","\n","When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project."]},{"cell_type":"code","metadata":{"id":"hCMdzAvkf2nu","colab_type":"code","outputId":"cd0ea33d-cbd0-4b58-a54f-7a4950bd83f8","executionInfo":{"status":"ok","timestamp":1559044031090,"user_tz":-330,"elapsed":4335,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":8562}},"source":["# TODO: Build and train your network\n","\n","#model=models.vgg19(pretrained=True)\n","\n","#for param in model.parameters():\n","#    param.requires_grad=False\n","    \n","MY_model=models.resnet152(pretrained=True)\n","\n","for param in MY_model.parameters():\n","        param.requires_grad=False\n","    \n","device=torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","\n","print(device)\n","print(MY_model)"],"execution_count":28,"outputs":[{"output_type":"stream","text":["cuda\n","ResNet(\n","  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (relu): ReLU(inplace)\n","  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n","  (layer1): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","      (downsample): Sequential(\n","        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","  )\n","  (layer2): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","      (downsample): Sequential(\n","        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (3): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (4): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (5): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (6): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (7): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","  )\n","  (layer3): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","      (downsample): Sequential(\n","        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (3): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (4): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (5): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (6): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (7): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, 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(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (10): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (11): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (12): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (13): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (14): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (15): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (16): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (17): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (18): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (19): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (20): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (21): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (22): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (23): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (24): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (25): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (26): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (27): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (28): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (29): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (30): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (31): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (32): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (33): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (34): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (35): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","  )\n","  (layer4): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","      (downsample): Sequential(\n","        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n","  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",")\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"62OMW7-Jf2oH","colab_type":"code","colab":{}},"source":["# TODO: Save the checkpoint \n","classifier2=nn.Linear(2048,102)\n","\n","MY_model.fc=classifier2\n","loss_fn=nn.CrossEntropyLoss()\n","#loss_fn=nn.NLLLoss()\n","optimizer=optim.Adam(MY_model.fc.parameters(),lr=0.001)\n","scheduler=optim.lr_scheduler.StepLR(optimizer,step_size=5,gamma=0.1)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"I7-QlOnlzC3K","colab_type":"code","colab":{}},"source":["def train_model(model,criterion,optimizer,scheduler,epochs=25,device='cuda'):\n","    start=time.time()\n","    best_model=copy.deepcopy(model.state_dict())\n","    best_acc=0.0\n","    \n","    for e in range(epochs):\n","        print('epoch {}/{}'.format(e,epochs-1))\n","        print('-'*10)\n","        \n","        for mode in ['train','valid']:\n","            if mode=='train':\n","                scheduler.step()\n","                model.train()\n","            else:\n","                model.eval()\n","            \n","            running_loss=0.0\n","            running_corrects=0\n","            \n","            for inputs,labels in dataloaders[mode]:\n","                #inputs.to(device)\n","                #labels.to(device)\n","                inputs.cuda()\n","                labels.cuda()\n","                \n","                #print(device,type(inputs),type(labels),type(model),type(best_model))\n","                #\n","                optimizer.zero_grad()\n","                \n","                with torch.set_grad_enabled(mode=='train'):\n","                    \n","                    outputs=model(inputs.cuda())\n","                    #outputs.cuda()\n","                    pred_values,preds=torch.max(outputs,1)#retunrs values, indexes\n","                    loss=criterion(outputs.cuda(),labels.cuda())\n","                    \n","                    if mode=='train':\n","                        loss.backward()\n","                        optimizer.step()\n","                    \n","                running_loss+=loss.item()*inputs.size(0)\n","                running_corrects+=torch.sum(preds.cuda()==labels.data.cuda())\n","                \n","            epoch_loss=running_loss/dataset_sizes[mode]\n","            epoch_acc=running_corrects.double()/dataset_sizes[mode]\n","            \n","            print('{} Loss: {:.4f} Acc: {:.4f}'.format(mode,epoch_loss,epoch_acc))\n","            \n","            if (mode=='valid' and epoch_acc>best_acc):\n","                best_acc=epoch_acc\n","                best_model=copy.deepcopy(model.state_dict())\n","                \n","                print('----saving the model----')\n","                \n","                model.cpu()\n","                \n","                save_checkpoint('checkpoint.pth',model,optimizer,best_acc,e)\n","                model.cuda()\n","                \n","                #best_model.cuda()\n","        \n","        print()\n","    \n","    time_taken=time.time()-start\n","    \n","    print('Training complete in {:.0f}m {:.0f}s'.format(time_taken//60,time_taken%60))\n","    print('Best Validation Acc: {:.4f}'.format(best_acc))\n","    \n","    model.load_state_dict(best_model)\n","    return model\n","            \n","                \n","                \n","    "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"-u6sos_gdgQn","colab_type":"code","outputId":"7c639b0a-9d7b-440f-99ac-ec542d0a0365","executionInfo":{"status":"ok","timestamp":1559045804150,"user_tz":-330,"elapsed":1773935,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":1751}},"source":["MY_model.to(device)\n","model_final=train_model(MY_model,loss_fn,optimizer,scheduler,15,device)"],"execution_count":31,"outputs":[{"output_type":"stream","text":["epoch 0/14\n","----------\n","train Loss: 2.2974 Acc: 0.5121\n","valid Loss: 0.6226 Acc: 0.8667\n","----saving the model----\n","epochs 0 \t best accuracy 0.8667\n","!-!-checkpoint saved-!-!\n","\n","epoch 1/14\n","----------\n","train Loss: 0.9337 Acc: 0.7674\n","valid Loss: 0.4170 Acc: 0.8973\n","----saving the model----\n","epochs 1 \t best accuracy 0.8973\n","!-!-checkpoint saved-!-!\n","\n","epoch 2/14\n","----------\n","train Loss: 0.7157 Acc: 0.8106\n","valid Loss: 0.3723 Acc: 0.8936\n","\n","epoch 3/14\n","----------\n","train Loss: 0.6222 Acc: 0.8349\n","valid Loss: 0.3322 Acc: 0.9071\n","----saving the model----\n","epochs 3 \t best accuracy 0.9071\n","!-!-checkpoint saved-!-!\n","\n","epoch 4/14\n","----------\n","train Loss: 0.4452 Acc: 0.8880\n","valid Loss: 0.2097 Acc: 0.9499\n","----saving the model----\n","epochs 4 \t best accuracy 0.9499\n","!-!-checkpoint saved-!-!\n","\n","epoch 5/14\n","----------\n","train Loss: 0.4197 Acc: 0.8933\n","valid Loss: 0.2143 Acc: 0.9511\n","----saving the model----\n","epochs 5 \t best accuracy 0.9511\n","!-!-checkpoint saved-!-!\n","\n","epoch 6/14\n","----------\n","train Loss: 0.4260 Acc: 0.8916\n","valid Loss: 0.1983 Acc: 0.9523\n","----saving the model----\n","epochs 6 \t best accuracy 0.9523\n","!-!-checkpoint saved-!-!\n","\n","epoch 7/14\n","----------\n","train Loss: 0.4028 Acc: 0.8997\n","valid Loss: 0.2007 Acc: 0.9511\n","\n","epoch 8/14\n","----------\n","train Loss: 0.4001 Acc: 0.8977\n","valid Loss: 0.2035 Acc: 0.9499\n","\n","epoch 9/14\n","----------\n","train Loss: 0.3890 Acc: 0.9042\n","valid Loss: 0.1925 Acc: 0.9560\n","----saving the model----\n","epochs 9 \t best accuracy 0.9560\n","!-!-checkpoint saved-!-!\n","\n","epoch 10/14\n","----------\n","train Loss: 0.3722 Acc: 0.9084\n","valid Loss: 0.2109 Acc: 0.9462\n","\n","epoch 11/14\n","----------\n","train Loss: 0.3855 Acc: 0.9034\n","valid Loss: 0.2020 Acc: 0.9535\n","\n","epoch 12/14\n","----------\n","train Loss: 0.3688 Acc: 0.9125\n","valid Loss: 0.1928 Acc: 0.9548\n","\n","epoch 13/14\n","----------\n","train Loss: 0.3734 Acc: 0.9078\n","valid Loss: 0.1962 Acc: 0.9548\n","\n","epoch 14/14\n","----------\n","train Loss: 0.3884 Acc: 0.9052\n","valid Loss: 0.2041 Acc: 0.9462\n","\n","Training complete in 29m 32s\n","Best Validation Acc: 0.9560\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"b3qSTGHqx8BK","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"nPrFrz9DiO0E","colab_type":"code","outputId":"e26d588d-80a6-4255-cf9f-8e349920a573","executionInfo":{"status":"ok","timestamp":1559045804157,"user_tz":-330,"elapsed":549895,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["device"],"execution_count":32,"outputs":[{"output_type":"execute_result","data":{"text/plain":["device(type='cuda')"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"code","metadata":{"id":"r5Not-wWzEOF","colab_type":"code","outputId":"263a9299-ec9c-44d5-df61-86e91f296005","executionInfo":{"status":"ok","timestamp":1559045808699,"user_tz":-330,"elapsed":198118,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":70}},"source":["!ls"],"execution_count":33,"outputs":[{"output_type":"stream","text":["adc.json\t  flower_data\t\t\t flower_data.zip\n","cat_to_name.json  flower_data_original_test.zip  sample_data\n","checkpoint.pth\t  flower_data_test\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"BET1r9ruXBsW","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ZxlbleF5f2oA","colab_type":"text"},"source":["## Save the checkpoint\n","\n","Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: `image_datasets['train'].class_to_idx`. You can attach this to the model as an attribute which makes inference easier later on.\n","\n","```model.class_to_idx = image_datasets['train'].class_to_idx```\n","\n","Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, `optimizer.state_dict`. You'll likely want to use this trained model in the next part of the project, so best to save it now."]},{"cell_type":"code","metadata":{"id":"hoCynDVhtruh","colab_type":"code","colab":{}},"source":["def save_checkpoint(path,model,optimizer,best_acc,epochs):\n","    model.class_to_idx=image_datasets['train'].class_to_idx\n","    \n","    torch.save({\n","        \n","        'arch':'resnet152',\n","        'num_epochs':epochs,\n","        'best_acc':best_acc,\n","        'model_state_dict':model.state_dict(),\n","        'optim_state_dict':optimizer.state_dict(),\n","        'class_to_idx':model.class_to_idx\n","        \n","    },path)\n","    \n","    print('epochs {} \\t best accuracy {:.4f}'.format(epochs,best_acc))\n","    print('!-!-checkpoint saved-!-!')\n","    "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"SPUOJbHoqeH5","colab_type":"code","outputId":"db6b1d63-96e2-4f16-d1d4-fae1ee921784","executionInfo":{"status":"ok","timestamp":1559044010812,"user_tz":-330,"elapsed":1324,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":52}},"source":["save_checkpoint('checkpoint.pth',MY_model,optimizer,92,10)"],"execution_count":26,"outputs":[{"output_type":"stream","text":["epochs 10 \t best accuracy 92.0000\n","!-!-checkpoint saved-!-!\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"g55Va0hvqk6b","colab_type":"code","outputId":"6cda67ad-9385-478d-e563-dd72facf1d5e","executionInfo":{"status":"ok","timestamp":1558983659254,"user_tz":-330,"elapsed":3731,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}},"colab":{"base_uri":"https://localhost:8080/","height":70}},"source":["!ls"],"execution_count":0,"outputs":[{"output_type":"stream","text":["adc.json\t  flower_data\t\t\t flower_data.zip\n","cat_to_name.json  flower_data_original_test.zip  sample_data\n","checkpoint.pth\t  flower_data_test\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Uyl3BYPwuHYa","colab_type":"text"},"source":["# testing"]},{"cell_type":"code","metadata":{"id":"VP2vHRNWuGV3","colab_type":"code","colab":{}},"source":["testdir='flower_data_test'\n","test_data_transform=transforms.Compose([\n","    \n","    transforms.Resize(256),\n","    transforms.CenterCrop(224),\n","    transforms.ToTensor(),\n","    transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])\n","\n","])\n","\n","image_test_datasets=datasets.ImageFolder(testdir,test_data_transform)\n","\n","batch_size=16\n","\n","test_dataloaders=torch.utils.data.DataLoader(image_test_datasets,batch_size=batch_size,shuffle=True)\n","\n","test_dataset_size=len(image_test_datasets)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"nOqpgphyvs4g","colab_type":"code","colab":{}},"source":["def calc_test_acc(model):\n","    model.eval()\n","    model.cuda()\n","    running_corrects=0\n","    \n","    with torch.no_grad():\n","        for inputs,labels in test_dataloaders:\n","            #inputs,labels = inputs.cuda(),labels=labels.cuda()\n","            inputs=inputs.cuda()\n","            labels=labels.cuda()\n","            outputs=model.forward(inputs)\n","            predvalues,preds=torch.max(outputs,1)\n","            running_corrects+=torch.sum(labels.data==preds)\n","        \n","    acc=running_corrects.double()/test_dataset_size\n","        \n","    print('test accuracy',acc)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"pn70NaygwsGX","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"39d0aba3-d54f-458c-b2d5-14cf7c2dac74","executionInfo":{"status":"ok","timestamp":1559046208575,"user_tz":-330,"elapsed":9849,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}}},"source":["calc_test_acc(MY_model)"],"execution_count":38,"outputs":[{"output_type":"stream","text":["test accuracy tensor(0.9316, device='cuda:0', dtype=torch.float64)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"s9Gk4Pm-f2oz","colab_type":"text"},"source":["## Loading the checkpoint\n","\n","At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network."]},{"cell_type":"code","metadata":{"id":"4arEgFU-f2o2","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":8598},"outputId":"dbbc0f45-5a3d-4c56-dc9d-938c4387ab0c","executionInfo":{"status":"ok","timestamp":1559048432726,"user_tz":-330,"elapsed":3844,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}}},"source":["# TODO: Write a function that loads a checkpoint and rebuilds the model\n","def load_checkpoint(path,model,optimizer):\n","    print(path)\n","    checkpoint=torch.load(path)\n","    \n","    print('epochs {}\\n best accuracy {}'.format(checkpoint['num_epochs'],checkpoint['best_acc']))\n","    \n","    if checkpoint['arch']=='resnet152':\n","        model=models.resnet152(pretrained=True)\n","        \n","        for param in model.parameters():\n","            param.requires_grad=False\n","            \n","    else:\n","        print('arch error')\n","        return None\n","    \n","    model.class_to_idx=checkpoint['class_to_idx']\n","    \n","    classifier=nn.Linear(2048,102)\n","    model.fc=classifier\n","    \n","    model.load_state_dict(checkpoint['model_state_dict'])\n","    \n","    return model\n","\n","MY_model_2=load_checkpoint('checkpoint.pth',MY_model,optimizer)\n","\n","print(MY_model_2)"],"execution_count":43,"outputs":[{"output_type":"stream","text":["checkpoint.pth\n","epochs 9\n"," best accuracy 0.9559902200488998\n","ResNet(\n","  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (relu): ReLU(inplace)\n","  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n","  (layer1): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","      (downsample): Sequential(\n","        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","  )\n","  (layer2): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","      (downsample): Sequential(\n","        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (3): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (4): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (5): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (6): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (7): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","  )\n","  (layer3): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","      (downsample): Sequential(\n","        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (3): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (4): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (5): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (6): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (7): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (8): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (9): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (10): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (11): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (12): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (13): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (14): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (15): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (16): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (17): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (18): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (19): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (20): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (21): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (22): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (23): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (24): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (25): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (26): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (27): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (28): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (29): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (30): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (31): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (32): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (33): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (34): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (35): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","  )\n","  (layer4): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","      (downsample): Sequential(\n","        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace)\n","    )\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n","  (fc): Linear(in_features=2048, out_features=102, bias=True)\n",")\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IP4YdP60f2pR","colab_type":"text"},"source":["# Inference for classification\n","\n","Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called `predict` that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like \n","\n","```python\n","probs, classes = predict(image_path, model)\n","print(probs)\n","print(classes)\n","> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n","> ['70', '3', '45', '62', '55']\n","```\n","\n","First you'll need to handle processing the input image such that it can be used in your network. \n","\n","## Image Preprocessing\n","\n","You'll want to use `PIL` to load the image ([documentation](https://pillow.readthedocs.io/en/latest/reference/Image.html)). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training. \n","\n","First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) methods. Then you'll need to crop out the center 224x224 portion of the image.\n","\n","Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so `np_image = np.array(pil_image)`.\n","\n","As before, the network expects the images to be normalized in a specific way. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`. You'll want to subtract the means from each color channel, then divide by the standard deviation. \n","\n","And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html). The color channel needs to be first and retain the order of the other two dimensions."]},{"cell_type":"code","metadata":{"id":"JImrE2zOkXn0","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":176},"outputId":"336355cd-d8b4-43ac-b3da-0c4cc6d8cb2d","executionInfo":{"status":"ok","timestamp":1559049234361,"user_tz":-330,"elapsed":1047,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}}},"source":["ll=os.listdir()\n","ll"],"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['.config',\n"," 'flower_data_test',\n"," 'cat_to_name.json',\n"," 'adc.json',\n"," 'flower_data_original_test.zip',\n"," 'checkpoint.pth',\n"," 'flower_data.zip',\n"," 'flower_data',\n"," 'sample_data']"]},"metadata":{"tags":[]},"execution_count":45}]},{"cell_type":"code","metadata":{"id":"dCrPJG1Sk2Lc","colab_type":"code","colab":{}},"source":["uploadId = '1w26x8ZtouNfb2X9piy6cEqsgi83BNpCN'\n","uploaded = drive.CreateFile({'parents':[{'id': uploadId}] , 'title' : 'checkpoint.pth'})\n","uploaded.SetContentFile('checkpoint.pth')\n","uploaded.Upload()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XLrFqw-CktsR","colab_type":"code","colab":{}},"source":["from PIL import Image"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hrR6VLWT1Dr-","colab_type":"code","colab":{}},"source":["if img.size[0] > img.size[1]:\n","        img.thumbnail((10000, 256))\n","    else:\n","        img.thumbnail((256, 10000))\n","    # Crop \n","    left_margin = (img.width-224)/2\n","    bottom_margin = (img.height-224)/2\n","    right_margin = left_margin + 224\n","    top_margin = bottom_margin + 224\n","    img = img.crop((left_margin, bottom_margin, right_margin,   \n","                      top_margin))\n","    # Normalize\n","    img = np.array(img)/255\n","    mean = np.array([0.485, 0.456, 0.406]) #provided mean\n","    std = np.array([0.229, 0.224, 0.225]) #provided std\n","    img = (img - mean)/std\n","    \n","    # Move color channels to first dimension as expected by PyTorch\n","    img = img.transpose((2, 0, 1))\n","    \n","    return img"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Q630iC9-f2pU","colab_type":"code","colab":{}},"source":["def process_image(image):\n","    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n","        returns an Numpy array\n","    '''\n","    \n","    # TODO: Process a PIL image for use in a PyTorch model\n","    pil_img=Image.open(image_path)\n","    \n","    loader=transforms.Compose([\n","        transforms.Resize(256),\n","        transforms.CenterCrop(224),\n","        transforms.ToTensor()\n","    ])\n","    \n","    \n","    pil_img=loader(pil_img).float()\n","    np_img=np.array(pil_img)/225\n","    \n","    mean = np.array([0.485, 0.456, 0.406])\n","    std  = np.array([0.229, 0.224, 0.225])\n","    \n","    np_img=(np.transpose(np_img,(1,2,0))-mean)/std\n","    #np_img=(np_img-mean)/std\n","    \n","    \n","    np_img=np.transpose(np_img,(2,0,1))\n","    \n","    return np_img\n","    "],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"cOljJ141f2ph","colab_type":"text"},"source":["To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your `process_image` function works, running the output through this function should return the original image (except for the cropped out portions)."]},{"cell_type":"code","metadata":{"id":"BoSGU_rOf2po","colab_type":"code","colab":{}},"source":["def imshow(image, ax=None, title='Flower'):\n","    \"\"\"Imshow for Tensor.\"\"\"\n","    if ax is None:\n","        fig, ax = plt.subplots()\n","    \n","    # PyTorch tensors assume the color channel is the first dimension\n","    # but matplotlib assumes is the third dimension\n","    image = np.transpose(image,(1, 2, 0))\n","    \n","    # Undo preprocessing\n","    mean = np.array([0.485, 0.456, 0.406])\n","    std = np.array([0.229, 0.224, 0.225])\n","    image = std * image + mean\n","    \n","    # Image needs to be clipped between 0 and 1 or it looks like noise when displayed\n","    image = np.clip(image, 0, 1)\n","    \n","    image=image*255.0\n","    \n","    image=np.array(image,dtype=float)\n","    ax.imshow(image)\n","    \n","    return ax"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"2KjqTunJxiqL","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":615},"outputId":"88386afd-dbf5-4288-e0d3-8069981fe25d","executionInfo":{"status":"error","timestamp":1559054157044,"user_tz":-330,"elapsed":1416,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}}},"source":["image_path='flower_data/valid/28/image_05258.jpg'\n","ims=imshow(process_image(image_path))\n","plt.imshow(ims)\n","#plt.imshow(ims, clim=(0.064, 0.068))\n","#mod_img = ndimage.median_filter(ims, 20)\n","#plt.imshow(mod_img)"],"execution_count":102,"outputs":[{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-102-3565fc6e6c12>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mimage_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'flower_data/valid/28/image_05258.jpg'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#plt.imshow(ims, clim=(0.064, 0.068))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#mod_img = ndimage.median_filter(ims, 20)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-101-e9ae5b70f9ea>\u001b[0m in \u001b[0;36mimshow\u001b[0;34m(image, ax, title)\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mmean\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.485\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.456\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.406\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mstd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.229\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.224\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.225\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstd\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;31m# Image needs to be clipped between 0 and 1 or it looks like noise when displayed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,) (3,224,224) "]},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYfAlqQmDL0lNGHxJauJ/Acz2XLpusNoKAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"p5P9FHhw4PAS","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":287},"outputId":"969956b9-5852-4283-ecb7-05e716c8e623","executionInfo":{"status":"ok","timestamp":1559054680605,"user_tz":-330,"elapsed":4184,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}}},"source":["def process_image(image_path):\n","    ''' \n","    Scales, crops, and normalizes a PIL image for a PyTorch       \n","    model, returns an Numpy array\n","    '''\n","    # Open the image\n","    #from PIL import Image\n","    img = Image.open(image_path)\n","    # Resize\n","    if img.size[0] > img.size[1]:\n","        img.thumbnail((10000, 256))\n","    else:\n","        img.thumbnail((256, 10000))\n","    # Crop \n","    left_margin = (img.width-224)/2\n","    bottom_margin = (img.height-224)/2\n","    right_margin = left_margin + 224\n","    top_margin = bottom_margin + 224\n","    img = img.crop((left_margin, bottom_margin, right_margin,top_margin))\n","    # Normalize\n","    img = np.array(img)/255\n","    mean = np.array([0.485, 0.456, 0.406]) #provided mean\n","    std = np.array([0.229, 0.224, 0.225]) #provided std\n","    img = (img - mean)/std\n","    \n","    # Move color channels to first dimension as expected by PyTorch\n","    img = img.transpose((2, 0, 1))\n","    \n","    return img\n","\n","def imshow(image, ax=None, title=None):\n","    if ax is None:\n","        fig, ax = plt.subplots()\n","    if title:\n","        plt.title(title)\n","    # PyTorch tensors assume the color channel is first\n","    # but matplotlib assumes is the third dimension\n","    image = image.transpose((1, 2, 0))\n","    \n","    # Undo preprocessing\n","    mean = np.array([0.485, 0.456, 0.406])\n","    std = np.array([0.229, 0.224, 0.225])\n","    image = std * image + mean\n","    \n","    # Image needs to be clipped between 0 and 1\n","    image = np.clip(image, 0, 1)\n","    \n","    ax.imshow(image)\n","    \n","    return ax\n","\n","image_path = 'flower_data/valid/28/image_05258.jpg'\n","img = process_image(image_path)\n","imshow(img)"],"execution_count":105,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fcb71191e48>"]},"metadata":{"tags":[]},"execution_count":105},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhZxmXBzzSncjZ0U82C5Q1+DR/C5685Sr+Rq5ZD7/wcf8/uf3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4raScqH3jfBgmaeRLLq/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l1ghqnmGH1pucyIse2YZsx2ko5P6Bkmho9eAhKSvPvr4GoAua/H/DQk0RgqGcrxjxj0uIdwWYQ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\nvfB2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tiQokW4HljaiU2gXA4l6CM7EJOuqG16rItLQcye4RPSOdY607BnTDWUJPL5Sbm/u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Qywlw8yQir0JNmTU3e9Gu+/o1vcP3gIa9fvqCmhWTUr3He0fcrhmFFFyxDNHTdmhAHjewu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\nQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"xeHeKGpT0eWO","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"AcfIT8Lgx32T","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6738x3IAf2p0","colab_type":"text"},"source":["## Class Prediction\n","\n","Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.\n","\n","To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.\n","\n","Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n","\n","```python\n","probs, classes = predict(image_path, model)\n","print(probs)\n","print(classes)\n","> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n","> ['70', '3', '45', '62', '55']\n","```"]},{"cell_type":"code","metadata":{"id":"nZrHLUqif2p2","colab_type":"code","colab":{}},"source":["def predict(image_path, model, num_k=5):\n","    ''' Predict the class (or classes) of an image using a trained deep learning model.\n","    '''\n","    \n","    # TODO: Implement the code to predict the class from an image file\n","    model.eval()\n","    \n","    model.cpu()\n","    \n","    np_img=process_image(image_path)\n","    img_tensor=torch.from_numpy(np_img).type(torch.FloatTensor)\n","    img_tensor=torch.unsqueeze(img_tensor,0)\n","    probs=torch.exp(model.forward(img_tensor))\n","    \n","    top_probs,top_labs=probs.topk(num_k)\n","    \n","    top_probs=top_probs.detach().numpy().tolist()[0]\n","    top_labs=top_labs.detach().numpy().tolist()[0]\n","    \n","    \n","    idx_to_class={val:key for key,val in model.class_to_idx.items()}\n","    \n","    top_labels=[idx_to_class[lab] for lab in top_labs]\n","    \n","    top_flowers=[cat_to_name[idx_to_class[lab]] for lab in top_labs]\n","    \n","    return top_probs,top_labels,top_flowers\n","    "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"VWGn4ElJAIDC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":70},"outputId":"be55da91-61ce-4981-84d9-b5d53e1a7dc7","executionInfo":{"status":"ok","timestamp":1559056535171,"user_tz":-330,"elapsed":1603,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}}},"source":["probs,classes,flowers=predict(image_path,MY_model_2)\n","print(probs)\n","print(classes)\n","print(flowers)"],"execution_count":113,"outputs":[{"output_type":"stream","text":["[2.3770978450775146, 0.0017433331813663244, 0.00016231260087806731, 0.00015321557293646038, 7.420249312417582e-05]\n","['28', '3', '43', '98', '68']\n","['stemless gentian', 'canterbury bells', 'sword lily', 'mexican petunia', 'bearded iris']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"6_B1gWX5AS7G","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"SB0JBqtDf2qI","colab_type":"text"},"source":["## Sanity Checking\n","\n","Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the validation accuracy is high, it's always good to check that there aren't obvious bugs. Use `matplotlib` to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:\n","\n","<img src='assets/inference_example.png' width=300px>\n","\n","You can convert from the class integer encoding to actual flower names with the `cat_to_name.json` file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the `imshow` function defined above."]},{"cell_type":"code","metadata":{"id":"2A-VsdeTf2qK","colab_type":"code","colab":{}},"source":["# TODO: Display an image along with the top 5 classes\n","def plot_solution(img_path):\n","    fig,(ax1,ax2)=plt.subplots(1,2)\n","    fig.figsize=(15,15)\n","    ims=imshow(process_image(img_path),ax2)\n","    probs,classes,flowers=predict(img_path,MY_model_2)\n","    ax1.barh(flowers,probs)\n","    plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"XzGpUfr1BhB-","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":269},"outputId":"8170fbcf-0d59-4934-fdc5-fa2602ad9ba6","executionInfo":{"status":"ok","timestamp":1559057063454,"user_tz":-330,"elapsed":2832,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}}},"source":["plot_solution(image_path)"],"execution_count":132,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAb0AAAD8CAYAAADjXXo5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\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Z4o2wWPf4pmWXlpz0X00yH+Xlz1zxu7/habbaME2Bpnc0rvIQiyq77UjjPGW4gxchRcGK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WvBqBWYhlYCMSimqvGlIwrNguDIbltStukt3++7oLfTTjt9GfDvAh8WkQ9ut/2nlGD3/SLy7wMv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\nhxASgeaMv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size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Q-qLZU1fBvet","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":269},"outputId":"8904e095-20e7-4edc-a4cd-78bc69ae7e86","executionInfo":{"status":"ok","timestamp":1559057069074,"user_tz":-330,"elapsed":2722,"user":{"displayName":"Abhishek 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MalEKEJBZjDSYlxEZECkyITGdKMB3VbJFFa/jqA3/Cfbft5fq3/QRtDGC0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vzWjbVsLxUxGjLn/vvspQ6REc+BoqntJjgnn2zoKFcCMcwWhiNkz2Q1DYpwieRyJU6SUZLvM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\nDMMwhgJTWIZhGMZQ8P9M1dlv1tcvewAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"2mprDYhhB6KF","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":269},"outputId":"25f5fd3a-4ab7-4607-e8b8-e195aab27103","executionInfo":{"status":"ok","timestamp":1559057073798,"user_tz":-330,"elapsed":2183,"user":{"displayName":"Abhishek 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4MQw2RQHQjS90AK+WcTTfx1DWQ9JA+wBB7kOqMoB7wi6zHq6Gp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\nFuUBJ8807OjPYKVjoRyg3dxwf+9MunIa1+77Sq7/YmbQ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size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"j67D8mLWB95l","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":269},"outputId":"62eeb329-fbd6-48ff-f907-0344bbe0389e","executionInfo":{"status":"ok","timestamp":1559057080394,"user_tz":-330,"elapsed":3015,"user":{"displayName":"Abhishek 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zjdf3iAyTVCJDOl4ga41SSI4R9QLMZpUv/e6/5Xs+/jCYimPP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\nHMcZYP4/SC08iiK3bCkAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"oc_rhFMwCFmw","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"YClFxNzbBjGH","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":70},"outputId":"00fa168f-7225-4340-8d92-e707ac9f1056","executionInfo":{"status":"ok","timestamp":1559057091914,"user_tz":-330,"elapsed":5905,"user":{"displayName":"Abhishek Verma","photoUrl":"https://lh5.googleusercontent.com/-_viXdH9kapE/AAAAAAAAAAI/AAAAAAAAIcs/AqVX2VQPOIY/s64/photo.jpg","userId":"13993408888009186883"}}},"source":["!ls"],"execution_count":136,"outputs":[{"output_type":"stream","text":["adc.json\t  flower_data\t\t\t flower_data.zip\n","cat_to_name.json  flower_data_original_test.zip  sample_data\n","checkpoint.pth\t  flower_data_test\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"k_pZXNmYBjh1","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}